Mitigate fraud risks associated with VISA and Master credit cards by-Design experimentations, build, train and test ensemble stack models. Uncover fraud trends with advanced statistics to proactively adjust detection strategies.Communicate complex findings to drive data-driven decision-making.

Developed and implemented a document retrieval system leveraging large language models (LLMs) at Lowe’s. Utilized advanced natural language processing and machine learningtechniques to optimize data accessibility and enhance document management efficiency. Demonstrated expertise in LLM deployment, data structuring, and retrieval algorithms to supportstreamlined knowledge access and operational workflow improvements.Market Mix Optimization: Determines the effectiveness of marketing campaigns using advanced statistical models, ensemble methods, and Bayesian statistics. This project resulted in improved ROI by identifying the most impactful marketing channels and optimizing budget allocation.Sales Prediction: Developed models to predict sales at individual Lowe's stores during severe weather events. This model improved decision-making and inventory management, resulting in reduced stock outs and increased customer satisfaction.

Developed and implemented a machine learning ensemble model for Cisco to identify malicious network traffic. Continuously improved model performance through experimentation and testing. Established robust post-deployment checks, including data drift monitoring, to guarantee ongoing effectiveness.

The research involved- computation modeling of environmental footprints across various levels of the supply chain.- used optimization algorithms to determine the best energy mix under different scenarios.

-Course: ISE 521 Introduction to Operation Research and Industrial Engineering Topics.-Grading and helping students to solve and debug assignments in C++ and Python.